A Comparison of Univariate ARIMA and Multivariate to Estimate Absorption Pattern in Stronsium Tittanate Dop Variation

Authors(3) :-Dina Oktasari, Muhammad Nur Aidi, I Made Sumertajaya

ARIMA, VARIMA, and GSTARIMA are the models used to model the observation series and variable dop containing spatial dependence between its dop. More complex models do not guarantee the forecast result will be more accurate. Therefore, the aims of this study are to model and assess the accuracy of forecasting of the ARIMA, VARIMA, and GSTARIMA using space weight is normalization of cross correlation. It was applied to the absorption pattern in stronsium tittanate with variation of dop. Based on this study, the fitted model used were ARIMA, VARI, and GSTARI modeling using normalization of cross correlation with the order of observation series AR(2). In addition, the ARIMA model more accurate to forecast the absorption pattern in stronsium tittanate with variation of dop than VARI and GSTARI model.

Authors and Affiliations

Dina Oktasari
Department of Statistic, Bogor Agriculture University, Bogor, Indonesia
Muhammad Nur Aidi
Department of Statistic, Bogor Agriculture University, Bogor, Indonesia
I Made Sumertajaya
Department of Statistic, Bogor Agriculture University, Bogor, Indonesia

ARIMA, VARIMA, GSTARIMA, modeling, forecasting.

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Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 356-360
Manuscript Number : IJSRSET184967
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Dina Oktasari, Muhammad Nur Aidi, I Made Sumertajaya, " A Comparison of Univariate ARIMA and Multivariate to Estimate Absorption Pattern in Stronsium Tittanate Dop Variation, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.356-360, July-August-2018.
Journal URL : http://ijsrset.com/IJSRSET184967

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